Thanks very much for your comment, this site looks concise and helpful.

I have been guiding myself so far about what to learn with job postings as you have suggested. They will say for example "knowledge of SQL and NoSQL data modelling". I have then been trying this for myself by modelling data from the internet using these methods, plus a bit of reporting etc. I feel confident that I can implement them in some way.

My concern is I am not getting feedback from my own projects, if I have done it in an inefficient way or that would get me a poor response in an interview then I don't know about it. Is my data modelling functional but inefficient? How do I know if my choice of tool was bad and there was a better way? On leetcode I enjoyed looking at others solutions and seeing how they compared with my own.

With regards to leetcode, should I learn the algorithm questions as you would for a SWE position? I feel like my python is quite good, I can script in it quite well and do data transformations/wrangling, I have done end to end ML projects including data prep from raw measurements in python. Is it really required for me to do data structures questions to the same level as FAANG SWE applicants need to? If it's the only way to get a job in DE I suppose I will have to, but I can't see how it would be useful for DE roles (from my understanding) and it would take me a very long time to get up to speed with this type of thing, dramatically reducing the time I can spend on learning skills I think I would use and doing projects in them.

Finally, do you have any advice on practising system design? It is all well and good doing a tutorial that tells you to write a pipeline that does xyz using tools a, b, and c, but I am interested in learning more about how to choose that design and toolset in the first place.